code: def _get_tf_exploration_action_op(
      self,
      action_distribution: ActionDistribution,
      explore: Union[bool, TensorType],
      timestep: Union[int, TensorType],
  ) -> "tf.Tensor":
      per_slate_q_values = action_distribution.inputs
      all_slates = action_distribution.all_slates
  
      exploit_action = action_distribution.deterministic_sample()
  
      batch_size, num_slates = (
          tf.shape(per_slate_q_values)[0],
          tf.shape(per_slate_q_values)[1],
      )
      action_logp = tf.zeros(batch_size, dtype=tf.float32)
  
      # Get the current epsilon.
      epsilon = self.epsilon_schedule(
          timestep if timestep is not None else self.last_timestep
      )
      # A random action.
      random_indices = tf.random.uniform(
          (batch_size,),
          minval=0,
          maxval=num_slates,
          dtype=tf.dtypes.int32,
      )
      random_actions = tf.gather(all_slates, random_indices)
  
      choose_random = (
          tf.random.uniform(
              tf.stack([batch_size]),
              minval=0,
              maxval=1,
              dtype=tf.float32,
          )
          < epsilon
      )
  
      # Pick either random or greedy.
      action = tf.cond(
          pred=tf.constant(explore, dtype=tf.bool)
          if isinstance(explore, bool)
          else explore,
          true_fn=(
              lambda: tf.where(
                  choose_random, random_actions, exploit_action
              )
          ),
          false_fn=lambda: exploit_action,
      )
  
      if (
          self.framework == "tf2"
          and not self.policy_config["eager_tracing"]
      ):
          self.last_timestep = timestep
          return action, action_logp
      else:
          assign_op = tf1.assign(
              self.last_timestep, tf.cast(timestep, tf.int64)
          )
          with tf1.control_dependencies([assign_op]):
              return action, action_logp
  
file_path: ray/python/ray/rllib/utils/exploration/slate_epsilon_greedy.py
imports: from typing import Union
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.exploration.epsilon_greedy import EpsilonGreedy
  from ray.rllib.utils.exploration.exploration import TensorType
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
label_desc: Use image node_id 1 for calling the SlateEpsilonGreedy obj's underlying member method code with example usage: obj._get_tf_exploration_action_op(action_distribution, explore, timestep) and returns: action,  action_logp, action,  action_logp